I want to get subset of my dataframe if date is before 2022-04-22. The original df is like below df: I checked data type by df.dtypes and it told me ‘date’ column is ‘object’. So I checked individual cell using df[‘date’][0] and it is datetime.date(2022, 4, 21). Also, df[‘date’][0] < datetime.date(2022, 4, 22) gave me ‘True’ However, when I wanted
Tag: datetime
Plot a custom colormap next to iterative scatterplot
I have a set of coordinates that are arranged per year, and I scatterplot them. I create a custom colormap that reflects those years, but the issue is I can’t manage to plot the colorbar of that colormap, and replace the ticks by the values of a numpy array of datetime64. I have no idea how to do it since
Converting integer values to week (year-week format)
I am trying to convert one of the dataframes I have to year-week format to use it in my time series modeling, but I am not sure how would I be able to do this? Here is my code: Output- Desired O/p in week column should be in date time format. The datatype was an int in the 1st dataframe,
Checking overlaps between two columns of datetime type in Pandas DataFrame
I have a dataframe with two columns that are datetime objects (time_a and time_b). I need to check on a row-by-row basis if the elements of time_a or time_b for such row, are contained within any of the other intervals defined by the other time_a and time_b rows. That’s what I defined as ‘overlap’, if any period of work between
Trying to get ‘QS’ frequency in pandas for a datetime64[ns] gives attribute error
I am working with an external data source and I am trying to get Quaterstart(QS) frequency for a particular data field. I am providing a dummy data and code below. I am gettinng the following error when I run this Can someone please help me understand what’s happening here? ps: The data given here is dummy data and not the
Is there a better way to increment a timestamp column in a pandas dataframe?
I’m working with a large pandas dataframe and want to add a timestamp column which correlates to the value of another column. For example, the current dataframe looks like this: Server Hour server1 0 server2 0 server1000 0 server1 1 server2 1 and so on, with the hours column at ranging from 0-167, as they correlate to the hourly timestamps
Count number of days in each continuous period pandas
Suppose I have next df N03_zero (date_code is already datetime): Millions of rows with date_code assigned to some item_code. I am trying to get the number of days of each continuous period for each item_code, all other similar questions doesn’t helped me. The expected df should be: Once days sequence breaks, it should count days in this sequence and then
How can I figure out the average consecutive duration of “True” values in pandas df, per group?
With the following data, I think I want a column (DESIRED_DURATION_COL) to work out the duration (according to start_datetime) of consecutive Truths: project_id start_datetime diag_local_code DESIRED_DURATION_COL 1 2017-01-18 False 0 1 2019-04-14 True 0 1 2019-04-17 True 3 1 2019-04-19 False 0 1 2019-04-23 True 0 1 2019-04-25 True 2 1 2019-04-30 True 7 1 2019-05-21 False 0 This is
Python – Add time zone to timestamp
I use chrome timestamp and convert it do readable date but time isn’t right Seems to be timezone need to be added example for last_visit_time : 13292010189305268 Answer Assuming that Chrome timestamps denote microseconds since 1601 UTC, you’ll want to make your datetime aware: If you want to format it for a non-UTC timezone, add a conversion step:
How to solve datetime format error “time data %r does not match format %r”?
I’m trying to convert a date from string format to a datetime format. I think that I’m using the right format, but the error is still raised. Code example: Error: Answer Your code wasn’t reading the microseconds properly. You can read microseconds with %f Try using this code, this will fix the issue: